Plan the remediation route when a weakness in AI-driven financial data analysis is identified.
Financial Data Analysis Using Artificial Intelligence
A working programme in AI-driven financial data analysis for managers who have to deliver with existing resources.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
The audit trail behind AI-driven financial data analysis matters as much as the decision itself. Capital, liquidity and reputation are all exposed by weak handling of data analysis. Comparative studies of the financial and banking practice discipline across sectors find the same handful of failure points recurring. Participants take away a working set of documents supporting AI-driven financial data analysis, ready to be adapted internally. A common pattern is strong design of the practice within financial and banking practice paired with weak follow-through. The programme converts data analysis from an area of general awareness into a set of repeatable practices. The teaching approach is deliberately practical: participants build a control framework for AI-driven financial data analysis as they go. The programme suits teams tackling data analysis together as readily as individuals attending alone. The programme closes with an action plan for the financial and banking practice capability that each participant writes for their own organisation.
Expected Learning Outcomes
Build the audit evidence that demonstrates data analysis operated as designed.
Verify that improvements to AI-driven financial data analysis have held six months after they were introduced.
Define the risk appetite applying to data analysis and translate it into operating limits.
Recognise early indicators that AI-driven financial data analysis is drifting away from its intended design.
Assess the capital and liquidity implications of data analysis.
Draw practical lessons from failures in AI-driven financial data analysis without assigning blame that suppresses reporting.
Who Should Attend
Treasury and asset-liability staff managing AI-driven financial data analysis.
Department heads accountable for the results of data analysis.
Regulatory reporting analysts covering AI-driven financial data analysis.
Internal auditors reviewing how data analysis is designed and operated.
Risk managers responsible for AI-driven financial data analysis.
Finance managers reporting on data analysis.
Course Modules
AI-driven financial data analysis: regulatory obligation and supervisory expectation
2 sessions · 8 pointsSession 1Comparing AI-driven financial data analysis with recognised practice
- Identify where judgement in AI-driven financial data analysis is legitimate and where it is not.
- Confirm that those complying with data analysis understand why it exists.
- Check the legal and contractual exposure created by AI-driven financial data analysis.
- Set out how exceptions to data analysis are requested and approved.
Session 2Setting a limit on data analysis that will actually be respected
- Set escalation thresholds for AI-driven financial data analysis that work out of hours.
- Draft the minimum viable control framework for data analysis.
- Rank the weaknesses in AI-driven financial data analysis by consequence rather than by ease of fixing.
- Review concentration by counterparty, sector and geography inside data analysis.
Data analysis: risk appetite, limits and policy
2 sessions · 8 pointsSession 1The early warning on data analysis that arrives in time
- Identify the key controls over AI-driven financial data analysis and who tests them.
- Confirm reporting on data analysis reaches the committee that can act on it.
- Agree what will be standardised in AI-driven financial data analysis and what will not.
- Remove steps in data analysis that add effort without adding assurance.
Session 2What a supervisor will ask about data analysis, and in what order
- Review the pricing of AI-driven financial data analysis against the risk being assumed.
- Confirm segregation of duties across initiation, approval and settlement of data analysis.
- Verify reconciliation and settlement controls covering AI-driven financial data analysis.
- Assign responsibility for keeping documentation of data analysis current.
Data analysis: the control framework and segregation of duties
2 sessions · 8 pointsSession 1Keeping data analysis alive after the initial push
- Confirm regulatory reporting on AI-driven financial data analysis is complete, timely and reconciled.
- Confirm client due diligence standards applied to data analysis are current.
- Design the exception process for AI-driven financial data analysis and require a documented rationale.
- Reduce the variation in how data analysis is carried out between teams.
Session 2Evidencing that AI-driven financial data analysis worked as designed
- Translate the appetite for AI-driven financial data analysis into limits someone monitors daily.
- Agree who signs off data analysis and record that they did.
- Estimate the resource AI-driven financial data analysis requires to run as designed.
- Check the accounting treatment applied to data analysis against current standards.
Data analysis: stress testing and scenario analysis
2 sessions · 8 pointsSession 1What data analysis does to capital and liquidity under stress
- Compare the cost of AI-driven financial data analysis with the cost of its absence.
- Assess the capital consumed by data analysis under current and stressed conditions.
- State the risk appetite for AI-driven financial data analysis as a number, not an adjective.
- Test data analysis against a scenario the organisation would rather not model.
Session 2Testing data analysis before relying on it
- Test the procedure for AI-driven financial data analysis against a realistic scenario.
- Plan the sequence in which improvements to data analysis will be introduced.
- Record what was learned when AI-driven financial data analysis did not go as planned.
- Rehearse the briefing on data analysis that would follow an incident.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
We will contact you within one business day to confirm.